Identifying source material associated with food products using bill of material

ABSTRACT

Identifying one or more source units for one or more food items may include assigning a sequence of numbers to a plurality of source units received at a processing entity and assigning a sequence of numbers to a plurality of end product units processed from the plurality of source units at the processing entity. A production rate associated with the end product units may be determined from a bill of materials associated with the end product units. A probability distribution function may be generated using at least the production rate. The probability distribution function maps a range of the sequence numbers associated with the plurality of source units to a range of sequence numbers associated with the end product units. A conversion algorithm may be generated that uses the probability distribution function to determine a source unit associated with a selected end product unit.

FIELD

The present disclosure relates generally to computer implemented systemsand methods for product supply networks, and more particularly toidentifying source units of an end product, e.g. food.

BACKGROUND

The food industry network is vast and a large amount of processing goeson in producing food from the raw material to the end product which canbe consumed by consumers. Tracking food product to its inception and midstages may not be an easy task. Missing data and connections in betweenstages and/or locations of food production can hinder accurate tracking.This all leads to difficulty in identifying actual sources from whichcontaminated or problematic food came from. Further, traditionalstrategies for food incidents focus on efficiently responding to theincidents after they occur. To that end, the present disclosure isdirected to enabling on-going monitoring of food processing stages andsupport of proactive actions to prevent food incidents.

BRIEF SUMMARY

A method and system for identifying one or more source units for one ormore food product items may be provided. The method, in one aspect, mayinclude assigning a sequence of numbers to a plurality of source unitsreceived at a processing entity, and assigning a sequence of numbers toa plurality of end product units processed from the plurality of sourceunits at the processing entity. The method may also include determininga production rate associated with the food product units from a bill ofmaterials associated with the food product units, and generating aprobability distribution function by a processor using at least theproduction rate, the probability distribution function for mapping arange of the sequence numbers associated with the plurality of sourceunits to a range of sequence numbers associated with the food productunits. The method may further include identifying a source unitassociated with the selected food product by executing a conversionalgorithm that uses the probability distribution function to determinesource material associated with the selected food product unit.

A system for identifying one or more source units for one or more foodproduct item, in one aspect, may include a sequence of entry moduleoperable to assign a sequence of numbers to a plurality of source unitsreceived at a processing entity. A sequence of exit module may beoperable to assign a sequence of numbers to a plurality of food productunits processed from the plurality of source units at the processingentity. A bill of materials module may be operable to determine aproduction rate associated the end product units from a bill ofmaterials associated with the end product units. A conversion module maybe operable to generate a probability distribution function using atleast the production rate. The probability distribution function may befor mapping a range of the sequence numbers associated with theplurality of source units to a range of sequence numbers associated withthe end product units. The conversion module may be further operable togenerate a conversion algorithm that uses the probability distributionfunction to determine a source unit associated with a selected endproduct unit. The conversion module further may be operable to identifythe source unit associated with a selected end product unit using theconversion algorithm.

In another embodiment, a system and method of determining aggregateimpact of food hazard are provided. The method of determining aggregateimpact of food hazard, in one aspect, may include creating atraceability model that includes information associated with a productflow across organization borders. The method also may include creating adaisy chain model, the daisy chain model including informationassociated with relationships among at least one or more risk factors inthe organization and one or more organization's operation processes. Themethod further may include creating a hazard dynamic model includinginformation associated with dynamics of a single hazard over time andenvironment. The method also may include determining a plurality oforganizations impacted by an external risk event by tracing the productflow of a product associated with the external risk event acrossorganizations border in the traceability model, and identifying one ormore organization's operation processes associated with the determinedplurality of organizations impacted by an external risk event by tracingthe daisy chain model. The method further may include determining one ormore risk factors associated with the one or more organization'soperation processes using the daisy chain model.

A system for determining aggregate impact of food hazard, in one aspect,may include a traceability model that includes information associatedwith a product flow across organization borders. A daisy chain model maybe associated with an organization, the daisy chain model includinginformation associated with relationships among at least one or morerisk factors in the organization and one or more organization'soperation processes. A hazard dynamic model may include informationassociated with dynamics of a single hazard over time and environment. Amodule may be operable to determine a plurality of organizationsimpacted by an external risk event by tracing the product flow of aproduct associated with the external risk event across organizationsborder in the traceability model. The module may be further operable toidentify one or more organization's operation processes associated withthe determined plurality of organizations impacted by an external riskevent by tracing the daisy chain model, the module further operable todetermine one or more risk factors associated with the one or moreorganization's operation processes using the daisy chain model.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a diagram illustrating the transition event type that enablestracing of the products from end-to-end in one embodiment of the presentdisclosure.

FIG. 2 illustrates a sample scenario that shows the workings of thetransition event type of the present disclosure in one embodiment.

FIG. 3 is illustrates an example BOM of food product.

FIG. 4A illustrates components and a process that uses BOM to identifysource(s) of an end product in one embodiment of the present disclosure.

FIG. 4B shows an example distribution function that maps source to endproducts by sequence numbers in one embodiment of the presentdisclosure.

FIG. 5 is a flow diagram illustrating the method steps in one embodimentof identifying the source of an end product in one embodiment of thepresent disclosure.

FIG. 6A shows an example daisy chain model for enabling food hazard orrisk detection in one embodiment of the present disclosure.

FIG. 6B is a flow diagram that illustrates hazard impact calculatormethod in one embodiment of the present disclosure.

FIG. 6C illustrates food traceability network in one embodiment of thepresent disclosure.

FIG. 6D illustrates a daisy chain meta-model of food hazard that enablesqualitative analysis for inferring across different elements in anoperation process of producing the food from its raw material in oneembodiment of the present disclosure.

FIG. 6E illustrates elements in an operation process of producing thefood organized into a hierarchical structure in one embodiment of thepresent disclosure.

FIG. 6F illustrates an example of a hazard dynamics model that describesthe dynamics of a single hazard over time and environment in oneembodiment of the present disclosure.

FIG. 7A is an example of a temporal and context-based food producttransition network model in one embodiment of the present disclosure.

FIG. 7B illustrates a sample scenario that illustrates a temporal andcontext-based food product transition network model in one embodiment ofthe present disclosure.

FIG. 8 is a diagram illustrating a method in one embodiment of thepresent disclosure for using a temporal and context based food producttransition network to assist in identifying the source reason of problemfood products.

FIG. 9 illustrates components of a system in one embodiment, which usetemporal and context based food product transition network and theinteractions among them to complete the functions.

FIG. 10 illustrates detecting irregularity in food manufacturing in oneembodiment of the present disclosure.

FIG. 11 shows the normal distribution of an example milk-feed ratio ofinvestigated farmers based on available historical data in oneembodiment of the present disclosure.

FIG. 12 illustrates an example of a computer system, in which thesystems and methodologies of the present disclosure may be carried outor executed.

DETAILED DESCRIPTION

Conversion Traceability for Product Supply Network

Information Traceability Server (ITS) is computer system software thatis used to trace a product via a unique product code in one specificdomain. A mechanism is disclosed in the present application that usesthe ITS to trace a product from beginning to end of its lifecycle acrossmultiple domains, even when the product acquires a different productcode as it enters different domain. The mechanism of the presentdisclosure thus provides capability for end-to-end traceability of aproduct. In one aspect, the mechanism employs event-based techniques toenable the end-to-end tracing. In another aspect, using the event-basedtechniques, components of supply chain (e.g., different domains), forinstance, may be linked.

The following description uses food products as examples for describingthe tracing of the food products from their inception to consumption bythe consumers. However, the mechanism described herein may be utilizedfor other products as well.

ITS follows a global standard called the Electronic Product Code (EPC)Information Services (EPCIS) specification to implement an informationsystem, for example, dependent of the database system used to store andretrieve data. ITS collects and stores product related trace data onevery relevant event as check points. Types of events include an objectevent, aggregation event, quantity event, transaction event and atransition event. An object event represents an event that happened toone or more entities denoted by EPCs (Electronic Product Code). Forexample, when a food product is produced and assigned a new product code(e.g., p001), an ObjectEvent.ADD event is generated and stored intoinformation system. A sample event may contain the following fields:

{Name=ObjectEvent; Action=ADD; Time=2009-8-18 11:18; EPCList={p001};Location=CompanyA.Workplace1; }

An aggregation event represents an event that happened to one or moreentities denoted by EPCs that are physically aggregated together, forexample, physically constrained to be in the same place at the sametime, as when cases are aggregated to a pallet. For instance, whenmultiple food products with different product codes are packaged into a“container” which has a separated product code, an AggregationEvent.ADDevent is generated. A sample event may contain the following fields:

{Name=AggregationEvent; Action=ADD; Time=2009-8-18 11:18;Location=CompnayA.Workplace2; parentID=container1;childEPCs={p001,p002,p003}}

A quantity event represents an event concerned with a specific quantityof entities sharing a common EPC class, but where the individualidentities of the entities are not specified. For example, an inventorycheck can generate an event to report the inventory level of one classof product. A sample event may contain the following fields:

{Name=QuantityEvent; Time=2009-8-18 11:18;epcClass=productClass1;Location=CompanyA.Warehouse1; }

A transaction event represents an event in which one or more entitiesdenoted by EPCs become associated or disassociated with one or moreidentified business transactions. For example, multiple products withdifferent product codes are associated with a transaction. A sampleevent may contain the following fields:

{Name=TransactionEvent; Action=ADD; Time=2009-8-18 11:18;bizTransactionList={transaction1}; epcList={p001, p002, p003}

A transition event represents an event in which a product transitions ortransforms into another product, or gets transported from one place toanother. For example, a livestock transforms into meat, meat transformsinto packaged pork, meat transforms into a canned product at a cannery,a canned product gets transported from a cannery to a distributioncenter and then to a store, etc.

Each product transformation or transportation is involved with twodomains, i.e., source and destination (or target) domains. For example,when a canned product is transported from a cannery to a store, thecannery is the source domain and the store is the destination domain.For product traceability, each domain may have an ITS system whichcollects and records every relevant event in the domain. A transitionevent of the present disclosure provides mapping between events in thesource domain and those in the destination domain, so that product traceinformation can be stitched (or linked) together across multiple ITSsystems deployed among multiple domains where a product goes through itslife time.

ITS uses EPC as an index or key to trace the product by searching allthe events with the same EPC. The mechanism of the present disclosureallows the tracing even when the EPC changes during the lifecycle of aproduct, for example, when it transforms from one type to another, forinstance, by implementing the transition event.

The transition event allows tracing of the link between EPCs of productand its dependent product. For example, when a farmer raises a livestockwith some feed product, a transition event can be used to express this“eat” semantic in EPCIS. Another example is a livestock and thetransformed meat product from the livestock that have different EPCs.Using the transition event, the livestock and the meat product may belinked and traced even though they have different EPCs. Thus, the endproduct that is meat can be tracked to its original livestock and evento the feeds used to raise that the livestock.

FIG. 1 is a diagram illustrating the transition event type that enablestracing of the products from end-to-end. Adding a new type of eventwhich may be referred to as a TransitionEvent is shown at 102. Atransition event type 104 is implemented that interacts with the EPCISevents 106 such as the object event 108, aggregation event 110, quantityevent 112, and transaction event 114.

Defining trigger rules to launch transition event generation action inthe event of a product crossing a boundary of ITS domains is shown at116. Trigger rules 117 are defined to launch transition event generationaction in the event of a product crossing a boundary of ITS domains.Domain here refers to a facility where a type of a product ismaintained. For example, a farm that raises livestock is an example of adomain. A meat processing facility that processes and maintains the meatproduct transformed from the livestock is another example of a domain.The trigger rules may be defined by taking the past events as parametersto describe the action to trigger. All the properties defined with anevent can be used as a condition in trigger rules. Further, the temporalrelationship of events can be used. Since users are allowed to definedomain specific event properties according to their working environment,properties such as ‘product manufacturing line x’ can be defined, andevents happening in the property can be specified in the trigger rules.An example trigger event happening in that property may be, ‘Product T(e.g., Sausage) produced at product manufacturing line 1 exiting theproduct manufacturing line x.” An example trigger rule associated withthat property and event may be, ‘When Product T (e.g., Sausage) producedat product manufacturing line 1 exits the line (which means the sausageis produced, packaged at the end of a manufacturing line, and thepackaged sausage an identity and ready for quality check and delivery)AND one hour before Product S (one of the source of the T, e.g.,packaged meat pieces) is scanned at product manufacturing line 1 entryA, then trigger an action with the two events as input’. A trigger ruledefines the trigger condition and the trigger action that is to beperformed if the condition is satisfied at runtime. That is, in responseto capturing one or more events that satisfy the condition portion ofthe rule, then the action portion of the rule is executed.

Defining actions to generate transition according to trigger events andrelated events found is shown at 118. Actions 119 to generate transitionaccording to trigger events and related events found are defined.Actions 119 may be implemented as programming codes, e.g., Java™ codeswhich has interfaces that could be used to generate new events such astransition events and put those events into the event queue.

Shown at 120, when a trigger rule becomes valid by one or more specificEPCIS events (e.g., object event, aggregation event, quantity event,transaction event), the corresponding one or more actions are executedto generate a transition event. For example, the object events shown at122 and 124 may fall within one or more of the defined trigger rules126, which then triggers an action 128 to create a transition event 130.That is, ObjectEvent 122 is used as a rule condition defined in Trigger126. Once it is triggered, event 122 and 124 (which is a related eventdefined in Trigger 126) will be taken as input for Action 128 togenerate a transition event 130.

Using standard EPCIS events to trace in a single domain and using thetransition events to link information across domains are shown at 132.Once the transition events (e.g., 140, 142) are generated, e.g., as in120, ITS or the like may query the events and trace a product acrossdifferent domains (e.g., 134, 136, 138). A transition event may includethe properties of the source and target of a transition process, withother information such as generation time, location, etc., asproperties. The information could be used as query condition. Thetransition events may be stored in a relational database tables, and thequeries can be defined as structured query language (SQL) query.

FIG. 2 illustrates a sample scenario that shows the workings of thetransition event type of the present disclosure in one embodiment. Thestages/steps column 202 shows the different domains where a product maybe processed and maintained. For instance, livestock such as a pig maybe raised in one domain (e.g., a farm), then moved to another domain(e.g., processing 1 facility) and transformed into cuttings. A cuttingfrom this domain (e.g., processing 1 facility) may be moved to yetanother domain (e.g., logistics) where the distribution to differentdomain is determined. Then the cutting may be distributed to stillanother domain (e.g., processing 2 facility) where the cutting may beprocessed into ham product. The ham product may be moved to anotherdomain (e.g., supermarket).

The product code column 204 shows the different product codes oridentifiers assigned to the product as it moves from domain to domain.For example, at the farm the livestock may be assigned codes p001 andp002, etc. At the processing 1 facility, the cuttings from the samelivestock may be assigned c001 and c002, etc. At the processing 2facility, the product made from the cuttings may be assigned h001 andh002, etc.

The EPCIS standard events column 206 shows the different eventsgenerated at different domains. For example, at the farm domain, an addobject event may be generated as each livestock is added into the farmdomain. Also, an observed object event may be generated when a livestockexits the farm domain. At processing 1 facility, add object event may begenerated when a cutting is added. An observed object event may begenerated when the cutting enters a different domain. The entries in the“EPCIS Standard Events” column in FIG. 2 have three elements: Eventtype, e.g., add, observe; Product code, e.g., p001 for pig 1, p002 forpig 2; Action, e.g., entry or exit to a domain. So, for example, thefirst event in the column shows Product p001 was added to Farm 1. Also,product 002 was added to Farm 1. Then, p001 exit from Farm 1 wasobserved. At the processing 2 facility domain, an add event object maybe generated as the ham products are produced from the cuttings. The“EPCIS Standard Events” column shows that three events are associatedwith the Processing 2 domain. Product c001 entry to PROC is observed,Products h001 and h002 exit from PROC are observed.

The trigger/action column 208 describes how a transition event may begenerated based on other events. For example, Trigger 1 was made whenproduct c002 exited from the location having code SH2. It triggers arule defined as Action 1 that maps a cut meat with a live stock, e.g.,c001 and c002 came from p001 and p002. Additionally, it records theinformation in ITS in the form of a transition event which shows themapping between source and target product code.

Extended EPCIS events column 210 shows the transition events generatedby the actions in response to the triggers in the trigger/action column.The transition event includes fields such as the source EPC and thetarget EPC. This way it is possible to trace from an output product allthe way back to a source product by following the transition event linksacross boundary borders. Links may be followed by matching the sourceand target EPCs.

Identifying Source Material Associated with Food Products Using Bill ofMaterial

To identify actual sources from which contaminated food product itemswere derived, the present disclosure, in another aspect, disclosespinpointing or detecting sources of food by using bill of material(BOM). BOM includes a list of raw materials and the quantities of eachneeded to manufacture a final product. BOM also may include a list ofsub-assemblies, intermediate assemblies, sub-components, components,and/or parts, that were generated from the raw material and that make upthe final product. In one embodiment of the present disclosure, foodsources are linked with a food product by BOM, the sequence number ofselected food product is recorded, the sequence number (as opposed totime stamp) range of food source units is generated based on the bill ofmaterial, and the food sources having sequence number within theestimated sequence range are identified.

FIG. 3 is illustrates an example BOM of a food product. The food productshown as an example is sausage 302. The production of sausage 302 maystart from whole meat rendered from livestock 304, which is processed at306 into pieces 308. The pieces 308 may be mixed and pickled at 318 withadditional raw material such as pigment 310, salt 312, starch 314, andsugar 316. The mixture 320 is filled at 324 into a casing 322, andsausage is produced as the final product. The BOM also records orincludes the amount of each material used to make the final product 302.The amounts of material are shown on the edges of the nodes connectingthe nodes of the BOM graph. The example of FIG. 3 shows that 120 kg ofmixtures 320 filled into 450 m of casing 322 produced 3000 pieces ofsausage 302.

FIG. 4A illustrates components and process that use BOM to identifysource(s) of an end product in one embodiment of the present disclosure.A sequence of entry component 404 records the sequence number of eachsource. A sequence of exit component 406 records a sequence number ofeach end product. The BOM component 408 keeps the relationship betweensources and products and production rate information. When the sourcesof a product need to be determined, the sequence number of the productis determined at 410. A conversion component 402 calculates the range ofsequence number of source units at 412. A conversion component 402 mapsend products with sources. Thus, source units are determined accordingto their sequence numbers at 414.

FIG. 5 is a flow diagram illustrating the method steps in one embodimentof identifying the source of an end product in one embodiment of thepresent disclosure. At 502, BOM and production rate distributions aredetermined. The BOM (Bill Of Material) is a data model that explainsrelationship between source units and products, including what isproduced with what and how many source units are required to produce aproduct (production rate), for example, as illustrated and explainedwith reference to FIG. 3. Using such data model, material and productionrate distributions associated with a product may be retrieved.

At 504, a conversion algorithm is created. The conversion algorithm isfor identifying source units associated with an end product of interest.A conversion algorithm contains several parameters (e.g., acceptableconfidence level). The parameters may be set when such an algorithm iscreated. Examples of conversion algorithm are described further below.

At 506, each source unit's sequence number is recorded. A source unit isone type of an input (there could be multiple input types) of the foodconversion process which is shown at 412 in FIG. 4A. For example, inmanufacturing, each source unit is assigned a sequence number. Forinstance, a device may be attached to a product manufacturing line entrywhich can count the number of input source units, increase the sequencenumber by one once a source unit is detected as input, and record thesequence number (the count) and associate the sequence number with thesource unit identifier into an information system. For example, fourinput source units entering a domain may be assigned sequence numbersS1, S2, S3 and S4.

At 508, the sequence number associated with each end product processedfrom the source units is recorded. The sequence number automaticallyincreases by 1 when a product is produced. For example, if seven endproducts are produced from the four source units as given in the aboveexample (S1, S2, S3, S2), those end products are assigned sequencesnumbers T1, T2, T3, T4, T5, T6 and T7. Thus, in one embodiment, thesource units and target units (end products) have separated sequencesets.

The steps 502 to 508 show a procedure for setting up the data (i.e.,source unit to end product associations using sequence numbers) in oneembodiment. Once setup, the data may be used offline to perform analysisfor tracing sources of an end product.

At 510, a product whose source unit needs to be determined is selected.That is, the selected product is the one of interest whose sources aredesired to be identified.

At 512, the conversion algorithm created at 504 is applied to estimatethe sequence range of the source units associated with the productselected at 510. The conversion algorithm automatically calculates thesequence range with a given input, which is the end product's sequencenumber. Recall that at steps 506 and 508, the sequence numbers wereassigned to the source units and end product units. At 512, by using analgorithm, correlation or linking of an end product to a source unit isperformed.

The conversion algorithm identifies the source units (and their sequencenumbers) for an end product (and its sequence number) at 512. Forinstance, given a production rate of a manufacturing machine, BOM, andtraining (empirical) data which has known mapping between source unitsequence numbers and end product sequence number, a distributionfunction (also referred to as a probability distribution or densityfunction) is determined for a given production rate and BOM using thetraining data. The distribution function may be modified as theproduction rate and/or BOM model change. For example, the parameters ofthe normal distribution are the mean and the variance as the graph ofthe associated probability density function is bell-shaped with peak atthe mean and tail shape determined by the variance. As the productionrate and the BOM model change, the mean and the variance of thedistribution function also may change. The exact function and mappingbetween the mean/variance and the production rate/BOM model isdetermined by the training data, for instance, as part of the mappingbetween source unit sequence numbers and end product sequence number.

The distribution function can be validated by using simulationtechniques. There may be multiple distribution functions, one for eachsource unit types, e.g., one for meat pieces, another one for starch,yet another one for sugar, etc.

Using the distributed function, the conversion algorithm can identifysource unit sequence numbers for an end product statistically, as shown,in FIG. 4B. Suppose the sequence number of the end product is 10800.Then using the distribution function shown in FIG. 4B, the conversionalgorithm determines that meat #4 was used to make this product with100% confidence. However, if the sequence number of the end productfalls between 11700 and 12600, the conversion algorithm may determinethe source units as being some of meat #4 and some of meat #5 with someconfidence level depending on the exact end unit sequence numberallocation in the distribution function. As noted above, there may bemultiple distribution functions for each different source types, and theconversion algorithm may identify source units used in an end productunit with probability or confidence level in a similar way.

A probability distribution function may be generated empirically byusing historical data. For instance, mean, standard deviation, and otherparameters that characterize the chosen probability distributionfunction (e.g., normal distribution function) may be computed fromhistorical data. A probability function may be also generated usingsimulation, for instance, by starting with a simple function requiringlittle assumption about the function, and refining it iteratively.

At 514, one or more source units having sequence number within the rangeare identified. For example, by querying the “sequence of entry”component (in FIG. 4, at 404) with the sequence range calculated in theprevious step (512), the source units can be identified.

The method of the present disclosure for inferring on food supplynetwork may be applied to any manufacturing process, in which forexample, a machine processes one or more types of source products intoone or more types of target products. Both source products and targetproducts are separated into discrete objects which can be labeled withan auto-increased sequence number before consumed and produced. Theobject is the level of granularity to trace. Objects of source productcan have different quantity in weight. When different source objects ofthe same type are mixed in machine for processing, it may decrease theconfidence level of determining correlation of target and sourceobjects. If different source product objects are mixed then they shouldbe taken as a combined object. The machine processes all source objectsin sequence. It may be assumed that the quantity of leftover in themachine is small enough to allow required confidence level indetermining the relationship of target products to source products. Themethod of the present disclosure infers the relationship between sourcematerials and target products, statistically. For example, because ofmeat leftover in machinery and the variations in its mechanism movement,it may not be possible to infer the relationship with 100% certainly.The uncertainty can be expressed statistically, i.e., by usingconfidence level. For example, with 90% confidence, the method mayconclude that a sausage piece of identifier 001 is made of meat from pigof identifier 007. The probability that a sausage piece 001 is made ofmeat from pig 007 is 0.9.

In one aspect, production rate is treated as a random variable havingknown distribution (typically normal distribution). A is source productA. B is target product B. Then the objects quantity Z of B that a unitweight of product A object can produce is following the normaldistribution Z=[N(μ,δ)], in which μ is the average number of product Bobjects that A (unit weight) can produce and δ is the variance. If anobject A's weight is W, then the quantity of B it can produce is Z*W. Zis the production rate (i.e., how many units of products can be producedwith one unit of source). It is treated as a random variable so that itis calculated with certain confidence level.

The following is an example of a conversion algorithm that is created.The algorithm computes the range of sequence numbers of source units.

Let p(i|x) be probability that product i was produced by source unit x,then it can be calculated as the following where Φ is cumulativedistribution function of the production rate distribution:

${p\left( {i❘x} \right)} = \left\{ {{\begin{matrix}{{\Phi_{{i\;\mu},{x\;\sigma^{2}}}^{\prime}(i)} - {\Phi_{{{({i - 1})}\mu},{{({x - 1})}\sigma^{2}}}^{\prime}(i)}} & \left( {i > 1} \right) \\{\Phi_{{i\;\mu},{x\;\sigma^{2}}}^{\prime}(i)} & \left( {i = 1} \right)\end{matrix}{\Phi_{\mu,\sigma^{2}}^{\prime}(i)}} = {1 - {\Phi_{\mu,\sigma^{2}}(i)} + {\Phi_{\mu,\sigma^{2}}(0)}}} \right.$

The confidence level can be further increased when considered batch-endconstraints.

In another aspect, a conversion algorithm may utilize simulation-basedapproach. For instance:

Having,

-   -   A1, A2, . . . An as source product objects 1 . . . n    -   B1, B2, . . . Bm as target product objects 1 . . . m    -   Z=[N(μ,δ)] which is the distribution of produce rate from unit        weight A to quantity of object B; ‘[ ]’ means round. That is,        Z=[N(μ,δ)] which is the production rate producing A to B.

Problem,

-   -   Given y(1<=y<=m), calculate the possibility of each A object        that could produce By, i.e., a target object.

Algorithm,

-   -   C[1 . . . n]=0; //C[1 . . . n] is an array to record the hit        count of each simulation of A1 . . . n for By    -   For a large iteration N, in each round, do        -   Set s=0; t=y;        -   While (t>0) do            -   s++; Simulate once Zs=[N(μ,δ)]*Ws; t−=Zs;            -   C[s]++; //C[s] hit once means in this simulation round,                As produces By        -   P[1 . . . n]=C[1 . . . n]/N //P[1 . . . n] is the            possibility of A1 . . . n to produce Bs

The above conversion algorithm can be explained as follows.

Assumption: Assume source food product is input as separate objectswhich can be counted in sequence, and also the target food product isoutput.

The problem is to compute the trace relationship between input andoutput with a level of confidence. A distribution function of thetranslation from source to target by weight may be obtained from thesubject matter expert's (SME's) experience based on the current process,i.e., the range estimation of the relationship between the quantity ofthe source, and the target produced by the source.

The conversion algorithm uses simulation based method to compute thepossibility of a target food object to the source food object. Thealgorithm executes the following sub steps a large number of times andrecords the hit times of target objects that belong to source objects.

Steps: (description of the algorithm)

-   -   Step 1: Get an instance number from the random distribution        function for source object 1, see how many target objects it        generates and correlate those target objects with source object        1.    -   Step 2: For each source object, repeat the Step 1, and so all        the correlation of target objects with source objects are        established.    -   Step 3: The correlation between source and target objects is        accumulated by hit time within the large loop of simulation.

Result: (the explanation of the output of the algorithm)

With the hit times and the total number of simulation loop, thepossibility of correlation between source and target can be computed.

Determining Aggregate Hazard Impact for a Food Traceability Network

In another embodiment of the present disclosure food hazard/riskanalysis model may be provided that enables decision makers (e.g.,government or enterprise) to perform qualitative as well as quantitativeanalysis to determine cause and impact of food hazard.

There are generally two typical decision makers or business scenarios,although the present disclosure does not limit to only those two. One isfood a manufacturing enterprise; the other is a food safety relatedgovernment or administration agencies. Food enterprises are interestedin the hazard exposure analysis, that is, food enterprises may beinterested in knowing the outbreak of some hazards in markets, trying tounderstand whether and how they will impact the enterprise, or whatkinds of hazard will have impact on the enterprise. To government, oncea food safety event occurs, the government and administrators or thelike would want to quickly find the root cause for this event anddetermine the involved range of food brands and/or lots that are at highrisk. Such analysis helps in the recalling process and allocatingresponsible entities for the food safety event.

In one embodiment of the present disclosure, the food hazard/riskanalysis may be based on three categories of models: 1) Traceabilitymodel or information to record the trace of a product or product lotfrom primary producers to end consumers; 2) Daisy chain model to setupthe relationship between risk factor, risk event, material (product),business process, mitigation approach and organization; 3) Hazarddynamics about the growth/mitigation over time under differentconditions (e.g., temperature, moisture). The models may be implementedas a computer data structure or the like.

1) Traceability Model

Traceability model construction may be based on BOM (bill of material)as well as transaction record as described with reference to FIGS. 1 and2. An example of a traceability model (also referred to as traceabilitynetwork model) is shown in FIG. 6C. Each node (e.g., 1-17) shownrepresents a processing facility (or a stage shown in FIG. 2 at 202).The traceability network model is arranged in a hierarchical structurein one embodiment. The lines with arrows illustrate the flow of productsfrom one node (e.g., facility or stage) to another.

2) Daisy Chain Model

A daisy chain meta-model is shown in FIG. 6D, which may include aprocess model 620, an organization model 622, a risk factor model 632, amaterial model 624, a risk event model 630, a mitigation solution model628, a food safety measure model 626, and other items. In themeta-model, a process is a hub of the connection(s) between differentmodels (e.g., organization 622, risk factor 632, risk events 630,material 624, etc.). The process model 620 includes processes associatedwith an organization specified in the organization model 622. The riskfactor model 632 includes risk factors associated with an organizationand its processes. The risk event model 630 includes risk eventsassociated with an organization and/or its processes. In one aspect,risk factors may be considered as causes (e.g., as shown in hierarchicalmodel example in FIG. 6E); risk events may be considered as effects(e.g., Salmonella outbreak, food poisoning in specific locale). Thematerial model 624 includes the products or material associated with anorganization and/or its processes. The food safety measure model 626includes food safety measures taken or defined in or associated with thematerial associated with an organization and/or its processes. Themitigation solution model 628 includes mitigating solutions associatedwith an organization and/or its processes.

For example, to find the risk factors related with an organization, themethodology of the present disclosure may identify related processes 620of an organization 622, then identify related risk factors 632 of theseprocesses 620 via the links from 622 to 620 to 632. When there is ahazard or risk factor, internal searching may be performed via the daisychain meta-model to identify the related process. Then through theprocess model 620, hazard or risk factors of other model elements (e.g.,product, organization, etc.) linked to the process may be identified orinferred.

In the daisy chain meta-model, the lower level enterprise's risk factor(e.g., beef quality from a meet processing plant) may be associated withthe material risk event of down-flow enterprise (e.g., a hamburgermaking company). Such inter-enterprise relationships through associatedrisk factors and risk events may be captured in the traceability modelin FIG. 6C.

The daisy chain meta-model shown in FIG. 6D and an extended BOM (Bill ofMaterial) model may be used to further enhance or build the traceabilitymodel in FIG. 6C as follows. Unlike the traditional BOM, which capturesoperations and materials limited to an enterprise, the extended BOMmodel of the present disclosure may show operations, materials and theirlinkage across all the enterprises contributing to an end product, e.g.,sausage. This extended BOM model also may be used to build or enhancethe traceability model at the operation and material level. Forinstance, to build the traceability model at the enterprise level asshown in FIG. 6C, the methodology of the present disclosure may use thedaisy chain meta-model shown in FIG. 6D to translate and consolidateoperations (or processes) 620 and materials 624 into enterprises (ororganizations) 622. The linkages among enterprises in the traceabilitymodel in FIG. 6C may be inferred and drawn by following the risk factors632 and risk events 630 associated with the operations (or processes)620 given in the extended BOM model. Note that the relationship betweena risk factor and a risk event is that of cause and effect. Therefore, arisk event associated with a lower-level enterprise in the traceabilitymodel can become a risk factor to the next level enterprise.

Each model element in the daisy chain meta-model can be organized into ahierarchical structure with multiple levels. FIG. 6E illustrateselements in a process of producing food, organized into a hierarchicalstructure. Each model (e.g., organization, risk factor, risk, material,etc.) may be organized into a hierarchical structure with levels such ascategory, sub-category, component, element and others. Depending on aspecific industry, categories may be broken down to lower levels, andthe number of levels in a hierarchical tree may be variables dependingon the details needed. The hierarchy shown at 612 is an example of riskfactor (632 FIG. 6D) model. The example hierarchical structure of therisk factors model shown has a 3-level hierarchy. “Poor PersonalHygiene” can be called as a category. “Lack of appropriate handwashing”,“Bare hand contact with ready-to-eat-food”, etc. are sub-categories.

A daisy chain model may be adopted within an enterprise or anorganization, and shows how the external products and internalorganization operations impact the outbound food quality (or food hazardlevels). A sample daisy chain model is shown in FIG. 6A, which shows therelationship for hamburger making process with related internaloperational risk factors as an example. In FIG. 6A, the boxes connectedby solid lines with arrowhead denote the organization process, whichdescribe a hamburger making process. Organization processes are shown at361 in FIG. 6A. Each organization process may have one or more riskfactor associated with that process (for example, shown as connectedwith dotted lines with arrowhead). For example, in the process “cook thepatty” 650, there is a risk factor called “Poor Personal Hygiene” 652,which in turn are divided into three detailed risk factors such as “lackof appropriate handwashing” 654, “bare hand contact with read-to-eatfood” 656, and “ill food workers” 658. FIG. 6A shows other organizationprocesses and associated risk factors linked in a manner such that onecomponent in a daisy chain model can be traced from or to anothercomponent in the daisy chain model.

Generally, as the daisy chain model covers more organizations (companyA) and/or more food product (Food B), instances for those organizationsand/or food products are added to the model. In addition, the instancesof other entities (e.g., processes, risk factors, etc.) related to theadded instances (e.g., company A and/or food B) may be added to themodel for the analysis. While the instances in the model can be added,modified and deleted, the model itself remains the same. For aparticular traceability and hazard impact analysis, the portion of theinstance set of the model relevant to the analysis may be extracted(e.g., for a particular company, food product, or risk).

3) Hazard Dynamics Model

A hazard dynamics model shows hazard dynamics, i.e., the growth and/ormitigation over time under different conditions (e.g., temperature,moisture). A sample hazard dynamics model is shown in FIG. 6F, whichillustrates the percentage of infected eggs under different stockconditions (e.g., stock temperature, packing, etc.) after an egg hasbeen infected by Salmonella. Hazard dynamics models may be built basedon specifications provided by a domain expert from experience,experiments, and other factors.

The above-described models may be implemented as any type of datastructure or the like and stored in a computer storage medium such asmemory device and/or hard disk.

The three categories of models function complementarily in performingthe aggregate hazard impact analysis in one embodiment of the presentdisclosure. For instance, a traceability model (e.g., described abovewith reference to FIGS. 1 and 2, and also shown in FIG. 6C) tells aboutthe product flow across enterprises or organization border. A daisychain model (e.g., shown in FIGS. 6A, 6D and 6E) may be adopted withinan enterprise to model the impact relationship between product, riskfactor, risk event, organization process, mitigation approach, and/orothers. A hazard dynamic model (e.g., shown in FIG. 6F) describes thedynamics of a single hazard over time and environment.

In one aspect, the qualitative analysis may be performed based on awell-organized model (e.g., the combination of the above three models)that links the hazard factors with process and organization. Qualitativeanalysis may be performed on a root-cause relationship. A root-causerelationship in food supply chain may include two perspective: 1) Root:When there is a food safety issue/hazard, one needs to figure out theoriginal source of the issue, 2) Cause: When it is confirmed that thereis a food safety hazard, one would like to know the products that havebeen impacted or will be impacted by the identified food issue. Forexample, it is possible to discover impact range of a hazard over theentire food traceability network. An impact range of hazard or hazardimpact range refers to a range of products, for example, brands/lots ofproduct that are affected by a hazard incident. For example, when it isknown that one batch of cattle in a farm which have been sold one weekago were diseased, one would like to know which brands/lots of hamburgerhave the food quality issue. Food traceability network includes theproduct flow from origin facility to destination or target facility, forinstance, from a farm, to a meat processing plant, to a hamburgerenterprise, and even to a fast-food retailer.

Qualitative analysis of the present disclosure may include the followinghazard analysis. A hazard exposure analysis may identify all hazardsthat an enterprise is being exposed to. For example, an organization maybe viewed a collection of processes and operations. So, for example, ifall hazards exposed by all the processes of an organization areidentified, all hazards the organization is exposed to may beidentified. A hazard impact target analysis may identify all enterprisesthat are impacted by a given hazard. Hazard tracing analysis mayidentify the source of a given food safety event. The qualitativeanalysis may be based on the traceability model and daisy chain modeldescribed above. For hazard tracing analysis, a daisy chain model isused to identify the related inbound products. Then, the product flow ofthe related inbound products is identified using the traceability model.For each specific food chain node (e.g., meat processing plant), a daisychain model may be used to drill down to identify related risk factorsand/or events. Hazard exposure and impact target analysis may be basedon the daisy chain. For example, there may be two general scenarios:

1) If the daisy chain model is constructed for each enterprise, then thehazard exposure analysis may include listing all risk factors in therisk factor models of a given enterprise. Risk impact targeting analysismay include searching a process in the enterprise that contains thegiven hazard in the risk factor model. As mentioned above, “riskfactors” are causes of risks. A hazard is considered as a risk or a riskevent.

2) Otherwise, for hazard exposure analysis, the analysis may includeidentifying all organizations related with an enterprise, then findingorganization processes related with each organization, at least listingall risk factors related with these processes. For hazard impact targetanalysis, the process is the reverse. That is, the analysis may includefirst identifying related organization processes of a given hazardthrough the daisy chain model, then searching organizations related toor associated with these organization processes. All such identifiedorganizations are summarized.

In another aspect, a quantitative evaluation of the impact of a hazardmay be performed. The quantitative analysis of the present disclosure inone embodiment calculates the degree of the relationship and hazardlevel based on the qualitative analysis. The qualitative analysis wasdescribed above as providing the root-cause and traceabilityrelationship. The calculation flow for the quantitative analysis in oneembodiment is shown in FIG. 6B, which is a flow diagram that illustrateshazard impact calculator method in one embodiment of the presentdisclosure. At 604, the traceability model, daisy chain model and hazarddynamics model are built based on the organization context of the foodchain. At 606, all risk events of the food chains are identified. A riskevent refers to an external incident that might happen with somepossibility, such as bird flu, Salmonella contamination, etc. The riskevents identified at 606 include a subset of all risk events related tothe enterprises identified through traceability model analysis. Forinstance, initially, the risk events may be identified and collectedbroadly, say, there are Salmonella outbreak in City A, City B, City C—3risk events. Consider an enterprise (or organization) that is a grocerin City C, which carries food supplied by only local farmers which, inturn, only deals with only local suppliers. If the locality can beconfirmed, the risk events in City A and City B can be safely removedfrom consideration for this grocer. In the following analysis, themethod may only focus on the risk event in City C.

The risk events may be based on the user analysis scope or interest(e.g., the user would like to analyze how egg related risk events mightimpact the whole food chain), or based on real business environment(e.g., there is a bird flu outbreak, government wants to learn the riskdistribution). At 608, all risk events to each node are built based onthe traceability model and daisy chain model. A node refers to aprocessing facility or entity or a stage of processing. The risk eventsrelated to an enterprise may be built through risk impact targetinganalysis discussed above. Within an enterprise, the linking of a riskevent with an organization's process is performed through the daisychain model.

At 610, aggregated hazard impact is calculated by simulation from thehigher hierarchy level of the network. That is, according to thetraceability model, each node is traversed beginning with the node withsource risk events, then to the nodes which are directly connected thenode. For each node in the food chain, since each model is inhierarchical structure, the leaf node's hazard is calculated first, thenpropagated to the top node. Hazard dynamics function is used in thehazard calculation. A stochastic simulation approach also may beadopted.

The following description illustrates an example of the aggregate hazardimpact calculation. FIG. 6C illustrates food traceability network. Eachnode denotes the food entity, for example, node 1 can be a bakeryproviding different kinds of bread. At each node, the method of thepresent disclosure may identify all kinds of hazards and their relatedfactors which will cause the specific kind of hazard. For each hazard,the method of the present disclosure may use expert knowledge and/orhistorical record to get the standard hazard impact model/experiencefunction. Consider salmonella as an example of hazard. Node 1 is abakery providing different kinds of bread; Node 17 is chicken farmproviding eggs; Node 2, 3 and 4 are manufactures for sandwich; and Node6 is a retailer of quick food.

For node 1, the factors for Salmonella may be temperature (t1), processtime (p1), etc. Salmonella content of the bread at Node 1 may be modeledby expert knowledge, and the following result obtained:

${F_{1}( \cdot )} = {{F_{1}\left( {t_{1},p_{1},\ldots}\mspace{14mu} \right)} = {{\hat{F}}_{1} \cdot {f\left( \frac{{t_{1} - {\hat{t}}_{1}}}{{\hat{t}}_{1}} \right)} \cdot {f\left( \frac{{p_{1} - {\hat{p}}_{1}}}{{\hat{p}}_{1}} \right)}}}$where {circumflex over (F)}₁, {circumflex over (t)}₁ and {circumflexover (p)}₁ are the standard Salmonella content of the bread, standardtemperature and process time for the standard manufacture processesrespectively. f(.) is S-Function which is an experience function.

Similarly, salmonella content for Node 17 may be obtained by:

${F_{17}( \cdot )} = {{F_{17}\left( {t_{17},p_{17},\ldots}\mspace{14mu} \right)} = {{\hat{F}}_{17} \cdot {f\left( \frac{{t_{17} - {\hat{t}}_{17}}}{{\hat{t}}_{17}} \right)} \cdot {f\left( \frac{{p_{17} - {\hat{p}}_{17}}}{{\hat{p}}_{17}} \right)}}}$

For node 3, the hazard impact from its parent nodes will be consideredwith its own risk factors, such as salmonella content from Node 1 andNode 17, the environment of the sandwich process, the cooking time foreggs, etc. The following equation models the salmonella content at Node3:

$\begin{matrix}{{F_{3}( \cdot )} = {F_{3}\left( {F_{1},v_{1},F_{17},v_{17},e_{3},t_{3}} \right)}} \\{= {{\hat{F}}_{3} \cdot {g\left( {F_{1},v_{1}} \right)} \cdot {g\left( {F_{17},v_{17}} \right)} \cdot {f\left( {e_{3},{\hat{e}}_{3}} \right)} \cdot {f\left( {t_{3},{\hat{t}}_{3}} \right)}}}\end{matrix}$where {circumflex over (F)}₃, {circumflex over (t)}₃ and ê₃ are thestandard salmonella content of the sandwich, standard cooking time andstandard environment for the standard manufacture processes,respectively; v is the volume or weight of corresponding products; g(.)is S-Function which is an experience function; and f( ) are alsoexperience functions.

The following illustrates a qualitative analysis of the presentdisclosure using beef as an example. There may be many differentpotential traces from a cattle farm to an end consumer, for example:

-   -   1) cattle farm->meat processing plant->CPG (Consumer Product        Goods) enterprise->retailer store. Here hamburger as the end        product sold to a consumer is used as an example.    -   2) cattle farm->meat processing plant->retailer store. Here raw        beef as the end product sold to a consumer is used as an        example.

Different farms might have different sales channels and traces. Supposethat a food traceability model from cattle farms to retailer stores hasbeen generated. There may be many risk events that might impact finalproduct quality, e.g., diseased cattle, unqualified cooking machine,unsafe cooling inventory, etc.

1) A Hazard Exposure Analysis Example

A hamburger making enterprise wants to learn all the risk that it facesor may be exposed to. A traceability model helps the enterpriseunderstand the inbound risk from material source (e.g., beef, lettuce,tomatoes, etc.). A daisy chain model such as the one shown in 6A aidsthe enterprise to better understand the risk factors from theenterprise's operations (e.g., personal hygiene).

2) A Hazard Impact Targeting Analysis Example

A disease is identified with several cattle farms, and one brand cookingmachines is found whose temperature is lower than the designedtemperature. A government authority or another entity wants to learnwhich enterprises are impacted by the two hazards.

From the traceability model, the methodology of the present disclosuremay trace which meat processing plant the diseased cattle have beensent, and related beef batches that might have food safety issue. Thenthe beef batches may be traced to identify the CPG enterprise and theretailer that received the affected beef batches. Thus, the governmentcan qualitatively know the impact scope.

For the cooking machines example above, the daisy chain model of thepresent disclosure may be used to determine which organizationoperations are dependent on the cooking machines, and the relatedmanufacturing enterprises that employ those operations. Then theidentified enterprises may be used in the traceability model todetermine the impact range.

3) Hazard Tracing Analysis

Consider as an example, that some quality reports about several batch ofbeef have been circulated about certain CPGs. A government authority orother entity may use a traceability model of the present disclosure todetermine where those CPGs obtain their material sources. In response todetermining that most of the material sources come from a few cattlefarms, those farms may be tagged as being highly suspicious. Once theyare confirmed to have quality issue, then the government authority oranother entity can in turn use the traceability model to perform thehazard impact targeting analysis to determine the range of impactthroughout the supply chain.

The following description explains the quantitative analysis of thepresent disclosure in one embodiment using the above examples, forinstance, to calculate the aggregate risk to one brand hamburger.

-   1) A risk event that occurred is identified (e.g., salmonella    outbreak), referred to herein as an outstanding risk event.-   2) Using a daisy chain meta-model (FIG. 6D), all risk factors    associated with the outstanding risk event are identified.-   3) Using the daisy chain meta-model (FIG. 6D), all organizations    associated with the identified risk factors are identified.-   4) The traceability model may be further built up or enhanced using    the identified organizations.-   5) For each identified organization starting from the lowest-level    (e.g., raw material) to upper levels (end product) one level at a    time, hazard impact quantity is computed. The computed hazard impact    quantities of all identified organizations at a level are combined    to form an aggregated hazard impact quantity of an organization at    the next level in the traceability model in FIG. 6C. A function that    combines the hazard impact quantity may be a linear function, e.g.,    a weighted average function or others or combinations thereof.

Temporal Context-Based Food Product Transition Network Model

In yet another embodiment of the present disclosure, a temporal andcontext based food product transition network model and system areprovided that not only help investigate the common reason but alsodiscover the exact time point the problem is introduced. In thisdisclosure, a food product transition network model is also referred toas a food traceability network or food traceability network model orfood transition network model. A food traceability network of thepresent disclosure may support experts to analyze the cause and theproblem introducing time point by using timestamp and inference (e.g.,by using semantic network technology) on the food product transitionnetwork model. The food transition network may be enriched withadditional information useful for tracking. The additional informationmay include but is not limited to, contexts (e.g., feed, farm, logistic,market, etc.), events, timestamps, risk indicators, etc. The method ofthe present disclosure may effectively consolidate and keep allinformation useful for food issue tracking in the food traceabilitynetwork, instrument context to collect information useful for trackingproblems, and utilize inference and visualization to help the tracking.

As described above, a food traceability network provides informationabout the food product and its source, for example, including basicsupply chain information such as source ingredients, manufacturingprocess, delivery process, etc. In the present disclosure in oneembodiment, the food traceability network is enhanced by collectingvarious context data and adding to the trace network. The context datamay include data such as where the products or ingredients are produced,mixed, delivered such as plants, vehicles used for delivery, containersused during production, delivery, storage, etc. Additionally, events ineach context with timestamps and possible risk indicators and theirvalues in each context may be added. For a contamination problem, theentire augmented information space may be utilized to track thecause(s).

For example, if food product A is found contaminated, the foodtraceability network provides information including basic supply chaininformation such as source ingredients, manufacturing process, deliveryprocess, etc. Contexts, events and timestamp information is providedsuch as shipping container temperature, manufacture plant bacterialcount, when the manufacturing and delivery happened, what other productswere there in the same context close to that time. For instance, ifproduct A.001 is contaminated, the enhanced food traceability networkcan tell the problem might have been introduced in a delivery process.For example, through the delivery container temperature data, it ispossible to tell that the abnormal high temperature may have caused thecontamination and the contamination might have impacted other productsin the same container.

FIG. 7 is an example of a temporal and context-based food producttransition network model. Context is a container with boundary; the areaunder the title of the context is a swimlane. Left side boundary of thecontext is for the input, right side boundary is for the output. Withina context, there could be more than one type of input entities andoutput entities. Each entity is a product set that is identified by thesame product code.

The context could be a farm process, logistic process, a processingprocess or a finer granularity level like a processing equipment in aprocessing flow. The swimlane is a time-series modeled up down; thevertical position of the input and output entities represents the timethat the entities enter or exit the context (timestamp). Within acontext, the output and input entities could be of different types ofproduct, and they could be linked in a many-to-many relationship torepresent the transition relationship by a confidence level. Aconfidence level can be assigned based on one or more factors,including, but not limited to, supplier qualifications and track record,nature of the input, the electronic traceability provided on theingredients supplied. The output entity of a context could be linked toinput entity (the same type and product code, but could be differentquantity) of another context to represent the send/receive relationship.Risk indicators can be defined in context; risk indicators represent thequantity of a risk factor measure at time T. The context shown in FIG.7A may be represented graphically on a user interface display and/or asa data structure. An internally computing data structure may be storedin memory to represent such contexts.

FIG. 7B shows a sample scenario. Farm 1 (702) and Farm 2 (704) use thesame feed product to raise pigs, and the pigs are transported to Market1 (706) and Market 2 (708) by the same vehicle (710). One day, the samevehicle transports Pig 3 product to Market 2 (708). When Pig 1 and Pig 2are reported to have a problem, the temporal food product transitionnetwork model (network model) 700 can be used to help identify thepotential cause of separated food safety events. Through the networkmodel, if the problem is caused by some common raw materials or commondependent product, it can be identified, e.g., inferred. For example,Pig 1 and Pig 2 are both raised by Feed 1. Through the network model, ifthe problem is caused by the context that the products shared orco-existed that may cause the problem, the context could be identified,e.g., inferred. For example, Pig 1 and Pig 2 used to be transported inthe same vehicle. This can be discovered by checking the entry/exit timeof the pigs to see if they have the common time duration in the logisticcontext. If the problem is introduced by the Vehicle 1, then by defininga time scope, e.g., from 2009-8-1 to 2009-8-3, the network model can beused to identify (e.g., infer) what other products could be impacted bythe same problem, i.e., Pig 3 in this scenario, that was transportedusing the same vehicle within days of transportation of Pig 1 and Pig 2.When the sample network is extended by including more segments, partiesand products in the real-world, the network model may be used inidentifying or inferring potential common cause of multiple problem foodproducts

FIG. 8 is a diagram illustrating a method for using temporal and contextbased food product transition network to assist in identifying thesource reason of problem food products. Given a known problem in a foodproduct 804, the method uses the data in the temporal and context basedfood product transition network 802 to determine the possible cause orsource of the problem. At 806, food industry domain detection technologymay be used to determine whether the problem is caused by raw materialsor caused by the production or transport process environment. Briefly,food industry domain detection technology refers to the chemical,biologic methods which are defined to detect the potential issues ofspecific foods.

If it is determined that that problem is caused by raw materials thenthe food product transition network may be used to identify the sourcematerials product at 808 and 814. For example, at 808 all raw materialsfor the problem food product are identified by tracing the food producttransition network. All dependent products (PAi), i.e., the sourceproducts used to produce the target products and all those productswhich are used to produce the source products are identified. At 814,for all PAi, the common set which includes the suspicious products isidentified. For example, when downstream food products are identified asproblematic, for each such downstream food product, the source productson the produce chain can be identified by the transition network. Onceall the source products are listed for each product identified asproblematic, common sources among the downstream food products may beidentified to determine or pinpoint a source material or materials thatmay have caused the problem.

If is determined that the problem is due to the production or transportprocess environment, then at 810 and 812 the context information of thetransition network may be used to identify the possible context whichcaused this problem. For example, at 810, all context informationassociated with the problem product in its cycle is identified. At 812,the common set which is the suspicious context is identified. Forexample, if it is determined that both products p1 and p2 have problemsand that they were both transported by the same transport vehicle, thecommon suspicious context is that transport vehicle.

The data in the context (like the temperature captured by sensors) couldbe used with domain knowledge as risk priority foundation, and aspecific context could be identified for further investigation. Forexample, the context could be a container of a shipping vehicle whichhas sensors to record the status of the container. Thus status,including abnormal container status can be recorded and stored ascontext information. For example, if a problem is detected with frozenfood, then the abnormal temperature recorded of the container thatcontained the frozen food may signal that the container as suspect.

At 816, for each suspicious context, assume that it is the one causingthe problem, and estimate the downstream or final products that couldhave problems due to that context. At 818, validate whether theassumption about the suspicious context is correct by checking theestimated downstream or final products to determine whether thoseestimated downstream or final products actually have problems. If so,the assumption that the suspicious context is the one causing theproblem is correct. At 820, if the source of the problem is identifiedin the context, the flow logic in the method ends, otherwise, the flowlogic returns to step 816.

FIG. 9 is a system diagram illustrating components of a system in oneembodiment the interactions among them using a temporal and contextbased food product transition network. An end-to-end traceabilitydatabase 916 stores temporal and context information associated withfood products. A model instance creation component 918 processes theinformation contained in the database 916 and generates a temporal andcontext based food product network model instance 920. The end-to-endtraceability database contains information about the transition ofproducts as events. Once the events include information, e.g., the time,context, etc. the information may be extracted and converted intotemporal and context based food product network model instance.

With this model instance 920, a query component 922 may be used to querysource materials associated with a food product, based on the linkagebetween source and target food. Another query component 924 may be usedto query the common context of different food products (or differentbatches), for example, as shown in FIG. 7B.

A risk indicator component 926 may be used to query the informationbased on risk factors. An example of a risk factor may be thetemperature, e.g., an abnormal temperature of a container that iscaptured by a sensor. If food is sensitive to the environmenttemperature and it is identified as having a problem, then the abnormaltemperature of the container which contained the problematic food may beconsidered as a risk factor.

Risk indicator and problem relationship data 928 includes the types orkinds of factors that could cause specific problems and associated levelof confidence or possibility of the factors causing those specificproblems. The information for the risk indicator and problemrelationship data 928 may be obtained from subject matter experts orfrom historical data or others.

A prioritizer component 930 may use the query capability of components922, 924, 926 and the information from 928 to query and prioritize thequery result. For instance, the prioritizer component 930 based on theinformation about the identified problem product 902 may answersquestions that an expert users 906 might have such as the cause of theproblem and the time point of the problem 908 and potential problemproducts 912. Answer may includes the possible cause of the problem(e.g., by raw materials or by context); where it happened (e.g., in somespecific context). Through the information provided by temporal andcontext based food product transition network data, common evidence maybe identified based on the identified problem. Risk factors analysis oridentification may determine the abnormal context data which can beshown to an expert as reference.

Detecting Irregularity in Food Manufacturing by Using Conversion Pattern

In still yet another embodiment of the present disclosure, a detectionsystem and method may be provided that detects irregularity in foodmanufacturing. Each stage along the stages of generating the final foodproduct consumes different resources. Generally there are certainrelationships between the amount of resources consumed and theproduction. This relationship in the present disclosure is referred toas conversion rules. Conversion rules provide the trace relationshipbetween products and resources. In one aspect of the present disclosure,the resource consumption data and production data for a final productover a certain or predetermined time span are obtained. The obtaineddata is verified to determine whether the data is consistent with theone or more conversion rules that are relevant to the final product. Ifthe data is not consistent with the conversion rules, violations mayexist, and may be treated as suspect for testing.

Bill of resources (BOR) is a list of all resources (e.g., materials,utilities, even labor) and the quantities of each needed to manufacturean end item. An end item is also referred to as a final product.Compared with bill of materials (BOM), BOR is more like a manufacturingprocess concept which includes raw materials as input, intermediate andfinal products as output. BOR is a more general concept which representsrelationships of associated elements during the lifecycle of a making ofa product. It could be considered that BOR is more about the logicalprocess while BOM is about the physical process.

For instance, considering the feeding stage as a production process of aswine, a sample bill of resources of a swine can be modeled as follows:Pig feed and vaccine are direct resources which have detailedinformation associated with them, such as the origin, amount, andeffective date. In another aspect, the exact value of the amount of feedused to raise the swine is not easy to obtain, for instance because thefarmer may not expose the feed purchase and consumption information. Soin order to determine the information about the feed more accurately, anindirect resource such as the power consumption of the feeding systemmay be utilized for determining the amount of feed used to raise theswine.

FIG. 10 illustrates detecting irregularity in food manufacturing in oneembodiment of the present disclosure. At 1002, historical data is usedto perform data analysis at 1004. The historical data depends on thespecific scenarios in practice. Historical data may come from the pasttraceability system which may include all essential information alongthe food manufacturing process. Based on those data, statistic analysisand data mining technologies can be used determine the general patternbetween input and output in terms of a specific scenarios. For example,one historical data set may be about an A-type feeding system, which isused to feed B-type feeds to C-type pig in summer season. From thatscenario, one may be able to determine, for example, by applyingregression analysis techniques, that:

-   -   one C-type pig consumes 3-6 kilograms (kg) of B-type feed each        day; and    -   one kilogram of B-type feed consumes A-type feeding system's        power 0.1˜0.2 kilowatt-hour (KWH).

At 1010, the output of the data analysis is used to generate a rulemodeler. The rule modeler is used to build rules that define thevalidation of business variables based on the functional relationshipamong them. For instance, the relationships found from 1004 would bemodeled as a formal rule in 1010 for representing the functionalrelationships among the inputs and output, which would be used as basisto validate whether the ongoing food manufacturing data complies withthe rule. For example, “one C-type pig consumes 3-6 kg B-type feed eachday” is a finding from the data analysis component above, which can berepresented as a function. Denote t as time unit, B(t) as feed consumedat t, and C(t) as number of pig at t, then B(t)=[3,6]C(t). The functioncan be translated as a kind of formalized rules format. Using ObjectConstraint Language as a sample, the rule would be described as“pig.feedPerDay<=6 and pig.feedPerDay>=3”.

The rule modeler at 1010 may be generated also by using inputs from thesubject matter expert (SME) 1006, supervisors 1008 or the like. Thefunctions describing the relationship among business variables can bebuilt from SMEs' knowledge and assumptions.

At 1014, rule management is performed for determining the priority andvalue of each rule given a complete rule set. Furthermore the priorityand value may be adjusted based on feedback at 1012.

The rules formalized from 1010 compose a rule set. Each rule might beviolated, and some of the violation may cause more serious food safetyproblems while other may be not so significant. Thus, the rules may beprioritized according to their significance or degree to which whenviolated would affect the food safety. The priority of the rules is notunalterable. For instance, the feedback from different channel maytrigger the change of the priority of the rules. For example, inresponse to an occurrence of polluted milk powder event, the ruleassociated with the ratio of material milk and milk powder may be set athigher priority than other rules.

At 1016, a calculation engine or like processing unit or softwaredetermines whether the current data being analyzed conforms to theconversion rules generated at the rule modeler 1010 and managed by therule manager at 1014. For instance, the calculation engine may compare afood manufacturing or manufacturer's data with the rules formalized atstep 1010 to determine whether the data conforms to the rules. The totalvalue of the compliance of each food manufacturing or manufacturer maybe calculated according to the information and preset value calculationmethod.

For example, for monitoring the milk production process, there may befour types of relationships or rules to be considered: Milk feed ratio,Electricity milk ratio, Sanitizer milk ratio, Drug milk ratio. At 1010,the rules can be formalized in Object Constraint Language (OCL) format.Object Constraint Language (OCL) is a declarative language fordescribing rules that apply to Unified Modeling Language (UML) models.At 1014, each rule may be set with a different weight value forprioritizing the rules. For each rule, such as Milk feed ratio, theratio is expressed in a normal distribution. At 1016, the Milk feedratio of the food manufacturing or manufacturer to be monitored may becalculated from the daily data. The ratio can be mapped in normaldistribution (left edge, midpoint, right edge), and the mappingcorresponded to exact value. For example, consider a function of feedvs. milk. Assume there is benchmark data existing for this functionwhere the data is normalized and expressed as a normal distributionfunction, i.e., a normal distribution function of feed vs. milkcapturing the benchmark data. The data (e.g., daily data) from a farm(which may be referred to as “instance data” or “as-is” data) may becompared with the benchmark data. The comparison can be done by locatingthe as-is data points in the normal distribution function of thebenchmark data. If the as-is data points are significantly out of theacceptable range of the data points of the normal distribution function,there is a possibility of incompliance.

In one embodiment, the compliance score of this food manufacturing ormanufacturer with respect to this rule may be computed as being equal tothe mapped value of rule multiplied by the weight of the rule.

A sample is shown in Table 1. According to the total score, themanufacturers can be listed in sequence. Extending the above example,suppose there is such benchmark data normal distribution function forevery factor—feed, electricity, sanitizer, and vaccination drug, in thisexample. The value in the below table shows the as-is data value fromthe benchmark data value. In addition, a weight to each factor may betaken into account—shown in the “Weight” column in the table. The Scoreis calculated by combining these two, (between as-is and benchmarkvalue) and weights together. The calculation can be a linear functionlike weighted average. It also can be a non-linear function.

TABLE 1 Bill of resource Rule's Weight As-is Value Compliance Score MilkFeed Ratio 0.65 9.6 370 Electricity Milk Ratio 0.15 2.3 240 SanitizerMilk Ratio 0.1 4.5 330 Drug Milk Ratio 0.1 1.2 260 Aggregated ComplianceScore 335.28

Take Milk Feed Ratio (MFR) as an example. For example, FIG. 11 shows thenormal distribution 1102 (shown as bar graph) of the MFR of investigatedfarmers (or farmer companies) based on available historical data.

As value of the MFR gets higher (in the right edge), the suspicionbecomes is higher. The suspicion may be formalized as the compliancescore, for example, ranging from 0˜1000 as the curve 1104 shows. Givenas-is value of MFR is 9.6, the compliance score of this company on thisrule is 370 according to the normal distribution and assigned scoreshown in FIG. 11. In like manner, other compliance scores shown in Table1 may be obtained: Compliance Score of Electricity-Milk-Ratio (EMR) is240 when EMR is 0.15; Compliance Score of Sanitizer-Milk-Ratio (SMR) is330 when SMR is 0.1; Compliance Score of Drug-Milk-Ratio (DMR) is 260when DMR is 0.1. The aggregated compliance score of this company on therules set can be calculated by combining the compliance sore of eachrule and the weight of each rule in weight average:Aggregated ComplianceScore=(9.6*370+2.3*240+4.5*330+1.2*260)/(9.6+2.3+4.5+1.2)=335.28

At 1018, based on the calculation at 1016, reports may be generated, forexample, by report generator software. At 1020, analysis report may beprovided, for example, in the form of a printout, display on a screenvia a graphical user interface, etc.

An example of a conversion rule in a dairy product producing environmentmay be:

milk feed ratio (MFR) milk: (feed—2 kg)=3

This rule says that a cow can generate 3 kg milk if it consumes 3+2 kgof feed. For example, consider a cow that produces 30 kg milk per day.Based on the ratio, it needs to eat 30/3+2=12 kg of feed. Here 2 kg feedis the basic material to sustain the cow. Extra feed will generate milk.

An example of a compound conversion rule may be:

milk powder ratio (˜8)

The above rule says that powdered milk usually reconstitutes at a ratioof 1 to 8, that is, 1 cup of milk powder to about 8 cups of liquid milk.

Other examples of conversion rule may include feed-meat ratios (feedconversion ratios), for instance, broiler chicken requiring 3.4kilograms of feed to produce one kilogram of ready-to-cook meat, pigswith a feed to meat ratio of 8.4 to 1. For a chicken to lay 1 egg, itneeds to consume 3.8 kg of feed, so the ratio is 3.8 to 1. Cheeseproduction ratio may be 7.9 to 1.5. Tilapia, typically, 1.6 to 1.8;poultry has a feed conversion ratio of 2 to 4; ruminants can convert 7kg of grain to one kilogram of meat. These are observed variables as thesource and target, and there is steady relationship among them atrequired confidence level. It should be noted that those figures aregiven as examples only. The numbers may be different according todifferent situations such as the farm methods, types of feeds, animalvarieties, locations, etc.

The method described above can identify, as an example, that one cow mayproduce milk 40 kg per day, and one kg milk may produce 0.1 kg of powdermilk. One cow can be linked with 146 kg powder per year. If thecollected data associated with the final product has discrepancies fromthis rule, e.g., because the data varies too much from this estimation,the resulting final product may be suspect.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The systems and methodologies of the present disclosure may be carriedout or executed in a computer system that includes a processing unit,which houses one or more processors and/or cores, memory and othersystems components (not shown expressly in the drawing) that implement acomputer processing system, or computer that may execute a computerprogram product. The computer program product may comprise media, forexample a hard disk, a compact storage medium such as a compact disc, orother storage devices, which may be read by the processing unit by anytechniques known or will be known to the skilled artisan for providingthe computer program product to the processing system for execution.

The computer program product may comprise all the respective featuresenabling the implementation of the methodology described herein, andwhich—when loaded in a computer system—is able to carry out the methods.Computer program, software program, program, or software, in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: (a) conversion to anotherlanguage, code or notation; and/or (b) reproduction in a differentmaterial form.

The computer processing system that carries out the system and method ofthe present disclosure may also include a display device such as amonitor or display screen for presenting output displays and providing adisplay through which the user may input data and interact with theprocessing system, for instance, in cooperation with input devices suchas the keyboard and mouse device or pointing device. The computerprocessing system may be also connected or coupled to one or moreperipheral devices such as the printer, scanner (not shown), speaker,and any other devices, directly or via remote connections. The computerprocessing system may be connected or coupled to one or more otherprocessing systems such as a server, other remote computer processingsystem, network storage devices, via any one or more of a localEthernet, WAN connection, Internet, etc. or via any other networkingmethodologies that connect different computing systems and allow them tocommunicate with one another. The various functionalities and modules ofthe systems and methods of the present disclosure may be implemented orcarried out distributedly on different processing systems or on anysingle platform, for instance, accessing data stored locally ordistributedly on the network.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

Various aspects of the present disclosure may be embodied as a program,software, or computer instructions embodied in a computer or machineusable or readable medium, which causes the computer or machine toperform the steps of the method when executed on the computer,processor, and/or machine. A program storage device readable by amachine, tangibly embodying a program of instructions executable by themachine to perform various functionalities and methods described in thepresent disclosure is also provided.

The system and method of the present disclosure may be implemented andrun on a general-purpose computer or special-purpose computer system.The computer system may be any type of known or will be known systemsand may typically include a processor, memory device, a storage device,input/output devices, internal buses, and/or a communications interfacefor communicating with other computer systems in conjunction withcommunication hardware and software, etc.

The terms “computer system” and “computer network” as may be used in thepresent application may include a variety of combinations of fixedand/or portable computer hardware, software, peripherals, and storagedevices. The computer system may include a plurality of individualcomponents that are networked or otherwise linked to performcollaboratively, or may include one or more stand-alone components. Thehardware and software components of the computer system of the presentapplication may include and may be included within fixed and portabledevices such as desktop, laptop, server. A module may be a component ofa device, software, program, or system that implements some“functionality”, which can be embodied as software, hardware, firmware,electronic circuitry, or etc.

The embodiments described above are illustrative examples and it shouldnot be construed that the present invention is limited to theseparticular embodiments. Thus, various changes and modifications may beeffected by one skilled in the art without departing from the spirit orscope of the invention as defined in the appended claims.

1. A method for identifying one or more source material for one or morefood items, comprising: assigning a sequence of numbers to a pluralityof source material received at a processing entity, the sequence ofnumbers assigned in an order of receiving; assigning a differentsequence of numbers to a plurality of food product units processed fromthe plurality of source material at the processing entity, the differentsequence of numbers assigned in an order of processing; determining aproduction rate associated with the food product units from a bill ofmaterials associated with the food product units, the bill of materialsincluding at least a list of raw materials and quantities of the rawmaterials used to manufacture a final food product; generating aprobability distribution function, by at least one processor, using atleast the production rate, and mapping, by the at least one processor, arange of the sequence numbers associated with the plurality of sourcematerial to the range of sequence numbers associated with the foodproduct units based on the generated probability distribution function;receiving a selected food product whose source material is to bedetermined; and identifying, by the at least one processor, the sourcematerial associated with the selected food product by executing aconversion algorithm that uses the probability distribution function todetermine the source material associated with the selected food product.wherein the identifying includes identifying the source materialassociated with the selected food product with a level of confidence. 2.The method of claim 1, further including validating the probabilitydistribution function by simulation.
 3. The method of claim 1, whereinthe identified source material is used in recalling other food productsassociated with the source material.
 4. The method of claim 1, whereinthe probability distribution function is generated using data associatedwith a range estimation of a relationship between a quantity of a sourceand a target food product produced by the source.
 5. The method of claim4, wherein the data is obtained from a subject matter expert.
 6. Themethod of claim 1, wherein the probability distribution function changesas a function of raw materials and quantities of the raw materials usedto manufacture the food product units change.
 7. A system foridentifying one or more source material for one or more food items,comprising: at least one processor; a sequence of entry module, executedon the at least one processor, operable to assign a sequence of numbersto a plurality of source materials received at a processing entity, thesequence of numbers assigned in an order of receiving; a sequence ofexit module, executed on the at least one processor, operable to assigna different sequence of numbers to a plurality of food product unitsprocessed from the plurality of source material at the processingentity, the different sequence of numbers assigned in an order ofprocessing; a bill of materials module, executed on the at least oneprocessor, operable to determine a production rate associated the endproduct units from a bill of materials associated with the end productunits, the bill of materials including at least a list of raw materialsand quantities of the raw materials used to manufacture a final foodproduct; a conversion module, executed on the at least one processor,operable to generate a probability distribution function using at leastthe production rate, and mapping a range of the sequence numbersassociated with the plurality of source materials to the range ofsequence numbers associated with the food product units based on thegenerated probability distribution function, the conversion modulefurther operable to generate a conversion algorithm that uses theprobability distribution function to determine a source materialassociated with a selected food product unit, the conversion modulefurther operable to identify the source material associated with theselected food product unit using the conversion algorithm, wherein theconversion module identifies the source material associated with theselected food product unit with a level of confidence.
 8. The system ofclaim 7, wherein the probability distribution function is furthervalidated using simulation.
 9. The system of claim 7, wherein theidentified source material is used in recalling other food productsassociated with the source material.
 10. The system of claim 7, whereinthe probability distribution function is generated using data associatedwith a range estimation of a relationship between a quantity of a sourceand a target food product produced by the source.
 11. The system ofclaim 7, wherein the probability distribution function changes as afunction of raw materials and quantities of the raw materials used tomanufacture the food product units change.
 12. A non-transitory,computer readable medium, tangibly embodying a program of instructionsexecuted to perform a method of identifying one or more source materialsfor one or more food items, comprising: assigning a sequence of numbersto a plurality of source material received at a processing entity, thesequence of numbers assigned in an order of receiving; assigning adifferent sequence of numbers to a plurality of food product unitsprocessed from the plurality of source material at the processingentity, the different sequence of numbers assigned in an order ofprocessing; determining a production rate associated with the foodproduct units from a bill of materials associated with the food productunits, the bill of materials including at least a list of raw materialsand quantities of the raw materials used to manufacture a final foodproduct; generating a probability distribution function by a processorusing at least the production rate, and mapping a range of the sequencenumbers associated with the plurality of source material to a range ofthe sequence numbers associated with the food product units based on thegenerated probability distribution function; receiving a selected foodproduct whose source material is to be determined; and identifying thesource material associated with the selected food product by executing aconversion algorithm that uses the probability distribution function todetermine the source material associated with the selected end productunit, wherein the identifying includes identifying the source materialassociated with the selected food product unit with a confidence level.13. The program storage device of claim 12, further including validatingthe probability distribution function by simulation.
 14. The programstorage device of claim 12, wherein the identified source material isused in recalling other food products associated with the sourcematerial.
 15. The program storage device of claim 12, wherein theprobability distribution function is generated using data associatedwith a range estimation of a relationship between a quantity of a sourceand a target food product produced by the source.
 16. The programstorage device of claim 12, wherein the probability distributionfunction changes as a function of raw materials and quantities of theraw materials used to manufacture the food product units change.